Few-shot segmentation (FSS) aims to train a model which can segment the
object from novel classes with a few labeled samples. The insufficient
generalization ability of models leads to unsatisfactory performance when the
models lack enough labeled data from the novel classes. Considering that there
are abundant unlabeled data available, it is promising to improve the
generalization ability by exploiting these various data. For leveraging
unlabeled data, we propose a novel method, named Image to Pseudo-Episode (IPE),
to generate pseudo-episodes from unlabeled data. Specifically, our method
contains two modules, i.e., the pseudo-label generation module and the episode
generation module. The former module generates pseudo-labels from unlabeled
images by the spectral clustering algorithm, and the latter module generates
pseudo-episodes from pseudo-labeled images by data augmentation methods.
Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate that our
method achieves the state-of-the-art performance for FSS.

使用未标记数据生成伪序列的一种新方法，可提高少样本分割模型的泛化能力，取得了准确性方面的最新成果。